Agent-Based User-Adaptive Filtering for Categorized Harassing Communication

TL;DR

This paper introduces an agent-based personalized filtering system for categorized harassment in social networks, which adapts to user preferences and feedback to improve moderation effectiveness.

Contribution

It presents a novel agent-based framework that models user-specific tolerance levels and dynamically adjusts filtering thresholds for harassment content.

Findings

Adaptive agents increase filtering precision.

User satisfaction improves with personalized filtering.

Framework effectively distinguishes harassment categories.

Abstract

We propose an agent-based framework for personalized filtering of categorized harassing communication in online social networks. Unlike global moderation systems that apply uniform filtering rules, our approach models user-specific tolerance levels and preferences through adaptive filtering agents. These agents learn from user feedback and dynamically adjust filtering thresholds across multiple harassment categories, including offensive, abusive, and hateful content. We implement and evaluate the framework using supervised classification techniques and simulated user interaction data. Experimental results demonstrate that adaptive agents improve filtering precision and user satisfaction compared to static models. The proposed system illustrates how agent-based personalization can enhance content moderation while preserving user autonomy in digital social environments.